Image categorization is a well-known open-research problem in computer vision for which many solutions have been proposed [1-3]. In medical image applications, image categorization is relatively under-investigated but nevertheless important. The volume of digital images acquired in the healthcare sector for screening, diagnosis or therapy is very large and increasing steadily.
For example, ultrasound based fetal anomaly screening is usually performed at 18 to 22 weeks of gestation. Several images of fetal structures are acquired following a standardized protocol. This screening scan aims to determine whether the fetus is developing normally by assessing several ultrasound images of different fetal structures. The number of different structures that are imaged and acquired in a complete scan varies. The UK NHS Fetal Anomaly Screening Programme (FASP) recommends 21 views to be assessed and at least 9 images to be stored. The number of individual women undergoing a scan is often of the order of several thousands per department per annum. Most clinical departments save these scans to an archive system without any labeling of the objects present in an image. This means it is not possible to conveniently recall images of, say, body parts for later review or measurement. Nor is it possible to compare scans of the same fetus over time, or conduct automatic on-line measurement post-acquisition.
Although there are a number of methods which have been proposed to address different medical image categorization problems [4-6], very little research has been done in fetal ultrasound image categorization [4, 7, 8]. This may in part be explained because fetal ultrasound image categorization has unique challenges. The quality and appearance of the images vary for a number of reasons including variation of fetal position, sonographer experience, and maternal factors, all affecting the appearance of images. In addition, a fetal ultrasound image can contain one or more fetal and non-fetal structures. The non-fetal structures can serve as distractions to categorization of the images.
Further, existing medical image classification systems have difficultly discriminating between features of interest and misleading structures or features that can exist in a medical image. The features of interest can be anatomical features in the image. General classification methods apply to a whole image to output a single class or classification and fail to achieve this test robustly. They fail to ignore regions where misleading structures can exist. Moreover, existing classification methods tend to be based on one more prior medical images in which a feature of interest, for example an anatomical feature of interest, has been identified and tagged as representative of the feature and used as a basis for correlation to other medical images for classification. Such approaches however have difficulty distinguishing different structures in diverse scenes and structures varying in translation or intention and/or scaling from one image to another image.
Thus, there is a need for a system and method to automatically categorize biological and medial images acquired over time. There is also a need for a system to more quickly, efficiently and accurately process and categorize medical images. In particular there is a need for a system and method to categorize biological and medical images that is discriminative, for example that can be guided to look for features in representative regions of an image while ignoring regions where misleading structures or features exist and that is invariant to translation, orientation and/or scaling of features of interest that can allow distinguishing different structures in diverse scenes.